Predicting efficiency of Drosophila-trained ComiR on various datasets.

<p>(A) Self-test on the Drosophila Ago1-IP dataset consist of Set I (positive examples) and equal number of negative examples (from Set IV). (B) Performance on an external Drosophila Ago1-IP dataset consisting of Set III (positive examples) and the remaining of Set IV (negative examples). This Drosophila dataset was not used in training ComiR. (C) SN <i>vs.</i> threshold on an external <i>C. elegans</i> AIN-IP dataset (not an ROC curve due to inability to define a negative dataset). (D) Performance on an external human PAR-CLIP dataset. In all cases, TargetScan was used without the evolutionary conservation feature resulting in a binary outcome. For the human dataset the reader can find a continuous TargetScan ROC curve in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi.1002830.s003" target="_blank">Figure S3B</a>, plotted using the <i>context score</i>.</p

The effect of Fermi-Dirac model in miRNA target prediction.

<p>(A) Overlap of predicted targets from PITA and miRanda using a naïve combination of energy scores. (B) Target overlap between PITA and miRanda using the Fermi-Dirac energy score combination. (C) Receiver-operating Characteristic (ROC) curves of PITA and miRanda predictions with naïve (solid lines) and Fermi-Dirac (broken lines) energy score combination. AUC: area under the curve. Positive and negative sets were derived from the Ago1 IP data (Materials and Methods).</p

ComiR schema.

<p>ComiR schema.</p

<i>Comparison of SVM models for multiple miRNA targets</i>.

<p>Multiple miRNA target scores are combined using the naïve model (red dots) or the ComiR model (FD score or WSUM score). The comparison has been performed on the same datasets as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi-1002830-g003" target="_blank">Figure 3</a> with the exception of the <i>C. elegans</i> dataset, which has no proper ROC curve. Results are arranged by the difference the ComiR combination models offers over the naïve combination model. <i>P</i>: PITA, <i>M</i>: miRanda, <i>T</i>: TargetScan, <i>S</i>: mirSVR. AUC: area under the curve.</p

Additional file 7: of The molecular landscape of premenopausal breast cancer

PARADIGM analysis in The Cancer Genome Atlas (TCGA). Table S1. Pathways detected by gene set enrichment analysis (GSEA) with input of gene expression and copy number variation data for the PARADIGM algorithm. The nine columns correspond to the pathway name, size of the pathway, Enrichment score (ES) score, Normalized enrichment score (NES) score, nominal p value, false discovery rate (FDR) q value, Family-wise error rate (FWER) p value, and leading edge (typical GSEA output). Table S2. Pathways detected by GSEA with input of gene expression, copy number variation and methylation data for the PARADIGM algorithm. The nine columns correspond to the pathway name, the size of pathway, ES score, NES score, nominal p value, FDR q value, FWER p value, and leading edge (typical GSEA output). (XLSX 88 kb

Additional file 12: of The molecular landscape of premenopausal breast cancer

Supplementary methods. This file contains additional details of the analyses. (DOCX 36 kb

Additional file 11: of The molecular landscape of premenopausal breast cancer

Gene list for clustering premenopausal (preM) estrogen receptor-positive (ER+) tumors. Table S1. Gene list selected by sparse k-means algorithm in The Cancer Genome Atlas (TCGA) data. Table S2. Genes selected based on TCGA data that are also in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data for validation. Table S3. Fixed number of genes (n = 21), gene list being selected from sparse k-means in TCGA. Table S4. Gene list selected by semi-supervised algorithm in METABRIC. Table S5. Fixed number of genes (n = 21), gene list being selected by semi-supervised algorithm in TCGA. Table S6. Genes (n = 28) in the LumA cluster that are significantly different between clusters 1 and 3. (XLSX 37 kb

Transcriptome characterization of matched primary breast and brain metastatic tumors to detect novel actionable targets.

Background: Breast cancer brain metastases (BrMs) are defined by complex adaptations to both adjuvant treatment regimens and the brain microenvironment. Consequences of these alterations remain poorly understood, as does their potential for clinical targeting. We utilized genome-wide molecular profiling to identify therapeutic targets acquired in metastatic disease.Methods: Gene expression profiling of 21 patient-matched primary breast tumors and their associated brain metastases was performed by TrueSeq RNA-sequencing to determine clinically actionable BrM target genes. Identified targets were functionally validated using small molecule inhibitors in a cohort of resected BrM ex vivo explants (n = 4) and in a patient-derived xenograft (PDX) model of BrM. All statistical tests were two-sided.Results: Considerable shifts in breast cancer cell-specific gene expression profiles were observed (1314 genes upregulated in BrM; 1702 genes downregulated in BrM; DESeq; fold change > 1.5, Padj Conclusions: RNA-seq profiling of longitudinally collected specimens uncovered recurrent gene expression acquisitions in metastatic tumors, distinct from matched primary tumors. Critically, we identify aberrations in key oncogenic pathways and provide functional evidence for their suitability as therapeutic targets. Altogether, this study establishes recurrent, acquired vulnerabilities in BrM that warrant immediate clinical investigation and suggests paired specimen expression profiling as a compelling and underutilized strategy to identify targetable dependencies in advanced cancers.</p